A hidden Markov model for continuous longitudinal data with missing responses and dropout

Pandolfi, Silvia; Bartolucci, Francesco; Pennoni, Fulvia

doi:10.1002/bimj.202200016

Biometrical J

2023

DOI: 10.1002/bimj.202200016

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A hidden Markov model for continuous longitudinal data with missing responses and dropout

Silvia Pandolfi

Francesco Bartolucci

Fulvia Pennoni

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to the computational complexity of the results.

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2024

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Cited by 3 publications

References 61 publications

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Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data

Pennoni,

Bartolucci,

Pandolfi

2024

J Classif

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We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a given time occasion. Through this procedure, we achieve a dimensionality reduction by selecting the subset of the most informative responses for clustering individuals and simultaneously choosing the optimal number of these clusters corresponding to latent states. The approach is based on comparing different model specifications in terms of the subset of responses assumed to be dependent on the latent states, and it relies on a greedy search algorithm based on the Bayesian information criterion seen as an approximation of the Bayes factor. A suitable expectation-maximization algorithm is employed to obtain maximum likelihood estimates of the model parameters under the missing-at-random assumption. The proposal is illustrated via Monte Carlo simulation and an application where development indicators collected over eighteen years are selected, and countries are clustered into groups to evaluate their growth over time.

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Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data

Pennoni,

Bartolucci,

Pandolfi

2024

J Classif

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show abstract

Hidden Markov models for multivariate panel data

Neal,

Sochaniwsky,

McNicholas

2024

Stat Comput

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A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Kuraz Abebe,

Wang,

Guo

et al. 2024

Gene

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A hidden Markov model for continuous longitudinal data with missing responses and dropout

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to the computational complexity of the results.

Cited by 3 publications

References 61 publications

Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data

Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data

Hidden Markov models for multivariate panel data

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

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